The AI trust lifecycle

Trust is a lifecycle,
not a dashboard.

AI doesn't fail in one place. It fails across four stages — build, validate, operate, govern. ActaClad covers all of them, so trust travels with the system from the first commit to the final audit.

Book a demo → Explore AgentGuard
Four stages, four kinds of trust

Trust doesn't start in production.

Pick a stage to see how we cover it — from the code you write to the decisions it makes in front of real users.

Services · build
Plumbline · design-time
AgentGuard · runtime + governance
Stage 01 · AI Engineering

Design and build production-grade AI.

Agentic systems, RAG, AI platforms, and integration into the systems you already run — built by a senior team that designs, ships, and hardens, then stays until it works.

Prototype → productionEnterprise integrationEmbedded AI squads
Explore services →
Stage 02 · Plumbline · open source

Catch the defects before they ship.

Static analysis for LLM and agentic code — unbounded loops, missing fallbacks, unsafe tool calls, silent model swaps — caught in the IDE, at commit, and in CI, before anything reaches a user.

DeterministicNo telemetrySARIF · CI-native
View Plumbline →
Stage 03 · AgentGuard

Observe, secure, and evaluate every decision.

Once it's live, AgentGuard traces every instrumented interaction, blocks prompt injection and PII leakage in your own request path, and scores correctness continuously — one record per decision.

Trace & costRuntime guardrailsContinuous evaluation
Explore AgentGuard →
Stage 04 · AgentGuard

Turn runtime evidence into audit-ready governance.

Coverage mapped to NIST AI RMF, the EU AI Act, ISO 42001 and India's DPDP — derived continuously from real runtime evidence, with a hash-chained audit trail and an AI Bill of Materials you can export.

Compliance coverageTamper-evident auditAI Bill of Materials
Explore AgentGuard →

AgentGuard guards production; Plumbline guards the code before it gets there.

In production

Two real deployments. Names on request.

Anonymized until our customers are ready to be named — but real, and in production.

Customer-experience AI · production

An AI platform running agents across every customer channel

They handle customer conversations at scale — across calls, reviews, social, and email — with autonomous AI agents. As the agent fleet grew, "why did it respond that way?" became a question no single tool could answer. AgentGuard gives them one record per interaction — trace, guardrails, and a Trust Score — across every agent in production.

Observe, secure, and evaluate every agent from one place — instead of stitching three tools together.
Sensitive-data AI · production

An AI assistant over highly sensitive personal data

Their platform puts an AI assistant in front of personal information that first responders rely on in an emergency — data that cannot leak. AgentGuard's security and governance pillars fit the profile: PII protection in the request path, and audit-ready evidence that the controls are actually holding.

Prove the guardrails hold and the evidence exists — for AI that touches data you can't afford to get wrong.
In production
First customers, live — not a roadmap slide.
Open source
Plumbline is public on GitHub, Apache-2.0 — inspect it yourself.
20+ years
Founders from regulated-enterprise architecture, 20+ years each.
SOC 2
Type 1 in progress; a third-party pen test available under NDA.

Real, in production, described with permission and anonymized by request. Named case studies and metrics will be added as they're ready — never invented.

Talk to us

Bring one AI system. We'll show you what trust looks like.

Building your first production AI, hardening one that's live, or preparing for an audit — start with a conversation. No slideware. Straight answers.

Talk to an expert → See AgentGuard